{"id":4001,"date":"2023-07-14T05:45:40","date_gmt":"2023-07-14T05:45:40","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d1%80%d0%b8%d0%bc%d0%be%d1%80%d1%81%d1%8c%d0%ba%d0%b8%d0%b8-%d1%81%d1%8e%d0%b6%d0%b5%d1%82-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d0%b0-%d1%80%d1%8f%d0%b4%d0%ba%d1%96%d0%b2\/"},"modified":"2023-07-14T05:45:40","modified_gmt":"2023-07-14T05:45:40","slug":"%d0%bf%d1%80%d0%b8%d0%bc%d0%be%d1%80%d1%81%d1%8c%d0%ba%d0%b8%d0%b8-%d1%81%d1%8e%d0%b6%d0%b5%d1%82-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d0%b0-%d1%80%d1%8f%d0%b4%d0%ba%d1%96%d0%b2","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d1%80%d0%b8%d0%bc%d0%be%d1%80%d1%81%d1%8c%d0%ba%d0%b8%d0%b8-%d1%81%d1%8e%d0%b6%d0%b5%d1%82-%d0%ba%d1%96%d0%bb%d1%8c%d0%ba%d0%b0-%d1%80%d1%8f%d0%b4%d0%ba%d1%96%d0%b2\/","title":{"rendered":"\u042f\u043a \u043d\u0430\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u043b\u0456\u043d\u0456\u0439 \u0443 seaborn (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c)"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043d\u0430\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u043b\u0456\u043d\u0456\u0439 \u043d\u0430 \u043e\u0434\u043d\u0456\u0439 \u0434\u0456\u043b\u044f\u043d\u0446\u0456 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e seaborn \u0443 Python:<\/span><\/p>\n

 import<\/span> seaborn as<\/span> sns\n\nsns. lineplot<\/span> (data=df[[' col1<\/span> ', ' col2<\/span> ', ' col3<\/span> ']]\n<\/b><\/pre>\n
 \u0423 \u0446\u044c\u043e\u043c\u0443 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u0431\u0443\u0434\u0435 \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043e \u043a\u043e\u043d\u0442\u0443\u0440 \u0456\u0437 \u0442\u0440\u044c\u043e\u043c\u0430 \u0440\u0456\u0437\u043d\u0438\u043c\u0438 \u043b\u0456\u043d\u0456\u044f\u043c\u0438.<\/span><\/p>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0446\u0435\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u043c\u0430\u043b\u044e\u0432\u0430\u043d\u043d\u044f \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u043b\u0456\u043d\u0456\u0439 \u0443 Seaborn<\/strong><\/span><\/h2>\n
 \u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u043c\u0430\u0454\u043c\u043e \u0442\u0430\u043a\u0438\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u0438\u0445 pandas, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u044e \u043f\u0440\u043e \u043f\u0440\u043e\u0434\u0430\u0436\u0456, \u0437\u0434\u0456\u0439\u0441\u043d\u0435\u043d\u0456 \u0447\u043e\u0442\u0438\u0440\u043c\u0430 \u0440\u0456\u0437\u043d\u0438\u043c\u0438 \u0440\u043e\u0437\u0434\u0440\u0456\u0431\u043d\u0438\u043c\u0438 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u043c\u0438 (A, B, C \u0456 D) \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c \u0432\u043e\u0441\u044c\u043c\u0438 \u0440\u043e\u043a\u0456\u0432 \u043f\u043e\u0441\u043f\u0456\u043b\u044c:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' year<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8],\n                   ' A<\/span> ': [10, 12, 14, 15, 15, 14, 13, 18],\n                   ' B<\/span> ': [18, 18, 19, 14, 14, 11, 20, 28],\n                   ' C<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' D<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   year ABCD\n0 1 10 18 5 11\n1 2 12 18 7 8\n2 3 14 19 7 10\n3 4 15 14 9 6\n4 5 15 14 12 6\n5 6 14 11 9 5\n6 7 13 20 9 9\n7 8 18 28 4 12\n<\/span><\/span><\/strong><\/pre>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e lineplot()<\/strong> \u0432\u0456\u0434 Seaborn, \u0449\u043e\u0431 \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0443, \u044f\u043a\u0430 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0447\u043e\u0442\u0438\u0440\u0438 \u0440\u044f\u0434\u043a\u0438 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u043d\u044f \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432, \u0437\u0434\u0456\u0439\u0441\u043d\u0435\u043d\u0438\u0445 \u043a\u043e\u0436\u043d\u0438\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u043e\u043c \u043f\u0440\u043e\u0442\u044f\u0433\u043e\u043c \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0440\u043e\u043a\u0443:<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#plot sales of each store as a line\n<\/span>sns. lineplot<\/span> (data=df[[' A<\/span> ',' B<\/span> ',' C<\/span> ',' D<\/span> ']])\n<\/strong><\/pre>\n
\"\u0431\u0430\u0433\u0430\u0442\u043e\u043b\u0456\u043d\u0456\u0439\u043d\u0438\u0439<\/p>\n
 \u041a\u043e\u0436\u0435\u043d \u0440\u044f\u0434\u043e\u043a \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043e\u0434\u043d\u043e\u0433\u043e \u0437 \u0447\u043e\u0442\u0438\u0440\u044c\u043e\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0456\u0432.<\/span><\/p>\n
 \u041b\u0435\u0433\u0435\u043d\u0434\u0430 \u0443 \u0432\u0435\u0440\u0445\u043d\u044c\u043e\u043c\u0443 \u043b\u0456\u0432\u043e\u043c\u0443 \u043a\u0443\u0442\u0456 \u0432\u043a\u0430\u0437\u0443\u0454, \u044f\u043a\u0438\u0439 \u043a\u043e\u043b\u0456\u0440 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u0454 \u044f\u043a\u043e\u043c\u0443 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0443.<\/span><\/p>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 palette<\/strong> , \u0449\u043e\u0431 \u0432\u043a\u0430\u0437\u0430\u0442\u0438 \u0432\u043b\u0430\u0441\u043d\u0456 \u043a\u043e\u043b\u044c\u043e\u0440\u0438 \u0434\u043b\u044f \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u043d\u0430 \u0433\u0440\u0430\u0444\u0456\u043a\u0443:<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#plot sales of each store with custom colors\n<\/span>sns. lineplot<\/span> (data=df[[' A<\/span> ', ' B<\/span> ', ' C<\/span> ', ' D<\/span> ']], palette=[' red<\/span> ', ' blue<\/span> ', ' purple<\/span> ', ' pink<\/span> '])<\/strong> <\/pre>\n
\"\"<\/p>\n
 \u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u043a\u043e\u043b\u044c\u043e\u0440\u0438 \u0440\u044f\u0434\u043a\u0456\u0432 \u0442\u0435\u043f\u0435\u0440 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u044e\u0442\u044c \u0447\u043e\u0442\u0438\u0440\u044c\u043e\u043c \u043a\u043e\u043b\u044c\u043e\u0440\u0430\u043c, \u044f\u043a\u0456 \u043c\u0438 \u0432\u043a\u0430\u0437\u0430\u043b\u0438 \u0432 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0456 \u043f\u0430\u043b\u0456\u0442\u0440\u0438<\/strong> .<\/span><\/p>\n
 \u042f\u043a\u0449\u043e \u0432\u0438 \u0445\u043e\u0447\u0435\u0442\u0435, \u0449\u043e\u0431 \u043a\u043e\u0436\u043d\u0430 \u0437 \u043b\u0456\u043d\u0456\u0439 \u0431\u0443\u043b\u0430 \u0441\u0443\u0446\u0456\u043b\u044c\u043d\u043e\u044e, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e pandas melt()<\/strong> , \u0449\u043e\u0431 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 DataFrame \u0443 \u0434\u043e\u0432\u0433\u0438\u0439 \u0444\u043e\u0440\u043c\u0430\u0442<\/a> , \u0430 \u043f\u043e\u0442\u0456\u043c \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043d\u0430\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438 \u043b\u0456\u043d\u0456\u0457:<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#plot sales of each store with custom colors\n<\/span>sns. lineplot<\/span> (x=' year<\/span> ', y=' value<\/span> ', hue=' variable<\/span> ', \n             data=pd. melt<\/span> (df,[' year<\/span> ']),\n             palette=[' red<\/span> ', ' blue<\/span> ', ' purple<\/span> ', ' pink<\/span> '])\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \u0422\u0435\u043f\u0435\u0440 \u043a\u043e\u0436\u043d\u0430 \u0437 \u043b\u0456\u043d\u0456\u0439 \u0441\u0443\u0446\u0456\u043b\u044c\u043d\u0430, \u0430 \u043d\u0435 \u043c\u0430\u0454 \u0432\u043b\u0430\u0441\u043d\u043e\u0433\u043e \u0441\u0442\u0438\u043b\u044e.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430<\/strong> . \u042f\u043a\u0449\u043e \u0443 \u0432\u0430\u0441 \u0432\u0438\u043d\u0438\u043a\u043b\u0438 \u0442\u0440\u0443\u0434\u043d\u043e\u0449\u0456 \u0437 \u0456\u043c\u043f\u043e\u0440\u0442\u043e\u043c seaborn \u0443 \u0431\u043b\u043e\u043a\u043d\u043e\u0442 Jupyter, \u0432\u0430\u043c, \u043c\u043e\u0436\u043b\u0438\u0432\u043e, \u0434\u043e\u0432\u0435\u0434\u0435\u0442\u044c\u0441\u044f \u0441\u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u0442\u0438 \u043a\u043e\u043c\u0430\u043d\u0434\u0443 %pip install seaborn<\/strong> .<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0445 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0430\u0445 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u0442\u0438\u043f\u043e\u0432\u0456 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u0432 Seaborn:<\/span><\/p>\n
 \u042f\u043a \u0434\u043e\u0434\u0430\u0442\u0438 \u043f\u0440\u0430\u0432\u043e \u0432\u043b\u0430\u0441\u043d\u043e\u0441\u0442\u0456 \u043d\u0430 \u0434\u0456\u043b\u044f\u043d\u043a\u0438 Seaborn<\/a>
 \u042f\u043a \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0440\u043e\u0437\u043c\u0456\u0440 \u0448\u0440\u0438\u0444\u0442\u0443 \u0432 \u0433\u0440\u0430\u0444\u0456\u043a\u0430\u0445 Seaborn<\/a>
 \u042f\u043a \u0432\u0456\u0434\u043a\u043e\u0440\u0438\u0433\u0443\u0432\u0430\u0442\u0438 \u0440\u043e\u0437\u043c\u0456\u0440 \u0444\u0456\u0433\u0443\u0440\u0438 \u0441\u044e\u0436\u0435\u0442\u0443 Seaborn<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u0431\u0430\u0437\u043e\u0432\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043d\u0430\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u043b\u0456\u043d\u0456\u0439 \u043d\u0430 \u043e\u0434\u043d\u0456\u0439 \u0434\u0456\u043b\u044f\u043d\u0446\u0456 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e seaborn \u0443 Python: import seaborn as sns sns. lineplot (data=df[[‘ col1 ‘, ‘ col2 ‘, ‘ col3 ‘]] \u0423 \u0446\u044c\u043e\u043c\u0443 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u0431\u0443\u0434\u0435 \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043e \u043a\u043e\u043d\u0442\u0443\u0440 \u0456\u0437 \u0442\u0440\u044c\u043e\u043c\u0430 \u0440\u0456\u0437\u043d\u0438\u043c\u0438 \u043b\u0456\u043d\u0456\u044f\u043c\u0438. \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0446\u0435\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456. \u041f\u0440\u0438\u043a\u043b\u0430\u0434: […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\n\u042f\u043a \u043d\u0430\u043c\u0430\u043b\u044e\u0432\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u043b\u0456\u043d\u0456\u0439 \u0443 Seaborn (\u0437 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